Application of Deep-learning and UAV for Field Surveying Corn Tassel

LIANG Yin-hao; CHEN Quan; DONG Cai-xia; YANG Chang-cai

doi:10.19303/j.issn.1008-0384.2020.04.014

Volume 35 Issue 4

Apr. 2020

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Article Navigation > Fujian Journal of Agricultural Sciences > 2020 > 35(4): 456-464

LIANG Y H, CHEN Q, DONG C X, et al. Application of Deep-learning and UAV for Field Surveying Corn Tassel [J]. Fujian Journal of Agricultural Sciences，2020，35（4）：456−464 doi: 10.19303/j.issn.1008-0384.2020.04.014

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LIANG Y H, CHEN Q, DONG C X, et al. Application of Deep-learning and UAV for Field Surveying Corn Tassel [J]. Fujian Journal of Agricultural Sciences，2020，35（4）：456−464 doi: 10.19303/j.issn.1008-0384.2020.04.014

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Application of Deep-learning and UAV for Field Surveying Corn Tassel

doi: 10.19303/j.issn.1008-0384.2020.04.014

LIANG Yin-hao^{1, 2
,},
CHEN Quan^{2, 3},
DONG Cai-xia^{2, 3},
YANG Chang-cai^{2, 3, 4
,
,}

1.
School of Mathematics, Northwest University, Xian, Shaanxi 710127, China
2.
Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
3.
Institute of Digital Research and Big Data/College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
4.
Key Laboratory of Smart Computing, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

Received Date: 2020-03-12
Rev Recd Date: 2020-04-15
Publish Date: 2020-04-01

Abstract

Abstract

Objective Deep-learning and computation were applied to analyze the images collected by drones on the status of tassel on corn plants in the field for estimating crop growth and forecasting production. Method Drones, or unmanned aerial vehicles (UAV), flying at a height of 25 m above corn crop in the field were used to generate RGB images showing the position and size of tassel on the plants at heading stage. Under the deep-learning framework of MXNet, the data sets on a 3-to-1 training-to-testing ratio were fed into 4 models of the ResNet50-based Faster R-CNN, the ResNet50-based SSD, the mobilenet-based SSD, and YOLOv3. The algorithms provided by the models were tested to intelligently extract information from the images for an accurate and rapid report on the status of corn tassel. Results mThe 236 UAV-collected images were cropped individually into 1024×1024 size. Those of poor quality were discarded to result in 100 labeled datasets using the Labelme software. The mAPs of the 4 models were 0.73, 0.49, 0.58 and 0.72, respectively. The highest accuracy rate of 93.79% on the test was obtained from the Faster R-CNN model, followed by 89.9% from SSD_ResNet50, 89.6% from SSD_mobilenet, and 20.04% from YOLOv3. On processing speed, SSD_mobilenet was the fastest at 8.9 samples·s⁻¹, followed by YOLOv3 at 3.47 samples·s⁻¹, SSD_ResNet50 at 7.4 samples·s⁻¹, and Faster R-CNN at 2.6 samples·s⁻¹. Among the 4 models, YOLOv3 was the largest, 241 Mb in size, while SSD_mobilenet the smallest 55.519 Mb. Conclusion Considering the scarcity of available resources on the airborne UAV platform, as well as the detection accuracy, processing speed, and size of the programs, the SSD_mobilenet model was selected as the choice for the field surveying of corn tassel by UAV.
- UAV,
- object detection,
- corn,
- tassel

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References(14)

References

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